Analysis - Novel Corona Virus 2019

Introduction

The following assessment is concentrated on …

Code

import pandas as pd
import numpy as np
import plotly.express as px

# Load the dataset
data = pd.read_csv('data/covid_19_data.csv')

# Data Transformation
data.columns = ['SerialNum', 'ObservationDate', 'Province/State', 'Country/Region', 'Last Update', 'Confirmed', 'Deaths', 'Recovered']

data['ObservationDate'] = pd.to_datetime(data['ObservationDate'])
data.fillna(0, inplace=True)

# Keep only numeric columns for aggregation
numeric_cols = ['Confirmed', 'Deaths', 'Recovered']

# Aggregate data by country and date
country_data = data.groupby(['Country/Region', 'ObservationDate'])[numeric_cols].sum().reset_index()

Code

print(country_data)

                       Country/Region ObservationDate  Confirmed  Deaths  \
0                          Azerbaijan      2020-02-28        1.0     0.0   
1                     ('St. Martin',)      2020-03-10        2.0     0.0   
2                         Afghanistan      2020-02-24        1.0     0.0   
3                         Afghanistan      2020-02-25        1.0     0.0   
4                         Afghanistan      2020-02-26        1.0     0.0   
...                               ...             ...        ...     ...   
87276  occupied Palestinian territory      2020-03-12        0.0     0.0   
87277  occupied Palestinian territory      2020-03-14        0.0     0.0   
87278  occupied Palestinian territory      2020-03-15        0.0     0.0   
87279  occupied Palestinian territory      2020-03-16        0.0     0.0   
87280  occupied Palestinian territory      2020-03-17        0.0     0.0   

       Recovered  
0            0.0  
1            0.0  
2            0.0  
3            0.0  
4            0.0  
...          ...  
87276        0.0  
87277        0.0  
87278        0.0  
87279        0.0  
87280        0.0  

[87281 rows x 5 columns]

Code

# Time Series Visualization
fig = px.area(country_data, x='ObservationDate', y='Confirmed', color='Country/Region',
              title='Confirmed Cases Over Time')
fig.show()

Code

# Bar chart for top 10 countries by confirmed cases
top_countries = country_data[country_data['ObservationDate'] == country_data['ObservationDate'].max()]
top_countries = top_countries.sort_values(by='Confirmed', ascending=False).head(10)
fig = px.bar(top_countries, x='Country/Region', y='Confirmed',
             title='Top 10 Countries by Confirmed Cases')
fig.show()

Code

# Choropleth map of global spread
latest_data = data[data['ObservationDate'] == data['ObservationDate'].max()]
fig = px.choropleth(latest_data, locations="Country/Region", locationmode='country names', 
                    color="Confirmed", hover_name="Country/Region", 
                    color_continuous_scale='Reds', title='Global Spread of COVID-19')
fig.show()

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# Pie chart of Confirmed cases by top 10 countries
fig = px.pie(top_countries, values='Confirmed', names='Country/Region', 
             title='Proportion of Confirmed Cases by Country')
fig.show()

Code

# Scatter plot of Confirmed vs Deaths
fig = px.scatter(country_data, x='Confirmed', y='Deaths', color='Country/Region',
                 title='Confirmed vs Deaths')
fig.show()